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Processes
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The Impact of Quality, Safety and Sustainability 4.0 on Manufacturing...
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The Impact of Quality, Safety and Sustainability 4.0 on Manufacturing Processes

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Manufacturing Processes and Systems".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 4501

Special Issue Editors

Prof. Dr. Kristína Zgodavová

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Guest Editor
Faculty of Materials, Metallurgy and Recycling, Technical University of Košice, 040 01 Košice, Slovakia
Interests: quality engineering; quality management; lean manufacturing; new product development; open innovation; e-learning; HEI accreditation
Prof. Dr. Gilberto Santos

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Guest Editor
Design School, Polytechnic Institute of Cavado and Ave, 4750-810 Barcelos, Portugal
Interests: certification; industrial engineering; industrial management; mechanical engineering; manufacturing; environment; production engineering; environmental engineering; quality management; innovation; integrated management systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jaroslav Nenadál

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Guest Editor
Department of Quality Management, Faculty of Materials Science and Technology, VSB—Technical University of Ostrava, 17. Listopadu 2172/15, 708 00 Ostrava-Poruba, Czech Republic
Interests: quality management area (including TQM; process management; excellence models; feedback loops related to quality)
Prof. Dr. Vidosav D. Majstorovic

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Guest Editor
Department for Production Engineering, Faculty of Mechanical Engineering, University of Belgrade, Kraljice Marije 16, 11120 Belgrade, Serbia
Interests: quality engineering; total quality management; production engineering; process development; product development; lean manufacturing; quality management; production planning; quality improvement; production metrology; Industry 4.0; AI and ML
Prof. Dr. José Alberto Eguren

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Guest Editor
Faculty of Engineering, Mondragon University, 20500 Arrasate, Spain
Interests: operations management; quality management; production management; production; lean manufacturing; production planning; quality engineering; lean six sigma; statistical process control; AMFE; circular economy in industrial operation management; Industry 4.0 in operation management
Prof. Dr. Hana Pacaiova

E-Mail Website
Guest Editor
Department of Safety and Quality, Faculty of Mechanical Engineering, Technical University of Kosice, Letná 1/9, 042 00 Kosice-Sever, Slovakia
Interests: safety management; risk assessment; maintenance management; prevention; continuity; sustainability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid development of technologies that fit into the Industry 4.0 (i4.0) framework allows us to realize our dreams. Over time, advances in computer science, especially simulations, help us to mimic the functioning of processes and products and complex systems in the real world, making these dreams more visible. With new technologies such as 3D printing, we can produce objects with complex geometries that we otherwise could not achieve with traditional technologies. Current threats to society's security and environmental sustainability require a thorough knowledge of related processes and their interactions and are a fundamental manifestation of society’s culture and maturity. The organization’s transformation to i4.0 is supported by digitalization and digital culture across the organization’s processes. Computer simulation has become an essential tool for improvement and innovation in many industries and services. At the level of production processes, it is about digital thinking, digital behavior, and digital skills. However, many opportunities for error arise during the engineering simulation process, leading to inaccurate predictions, erroneous decisions, and potential failure of the designed system.

This Special Issue on “The Impact of Quality, Safety and Sustainability 4.0 on Manufacturing Processes” focuses on high-quality work related to contemporary computer simulation and prediction of quality and safety of processes, products and systems; environmental, economic and social sustainability; the integrated management of organizations; engineering processes used to support the evaluation of process performance; and the impact of Quality 4.0 on organizations´ performance.

Prof. Dr. Kristína Zgodavová
Prof. Dr. Gilberto Santos
Prof. Dr. Jaroslav Nenadál
Prof. Dr. Vidosav D. Majstorovic
Prof. Dr. José Alberto Eguren
Prof. Dr. Hana Pacaiova
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • digital culture
  • prediction
  • process
  • quality
  • safety
  • simulation
  • sustainability
  • 3D printing

Published Papers (3 papers)

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Research

14 pages, 3798 KiB  
Article
Predictive Quality Analytics of Surface Roughness in Turning Operation Using Polynomial and Artificial Neural Network Models
by Peter Bober, Kristína Zgodavová, Miroslav Čička, Mária Mihaliková and Jozef Brindza
Processes 2024, 12(1), 206; https://doi.org/10.3390/pr12010206 - 18 Jan 2024
Viewed by 578
Abstract
The variability of the material properties of steel from different suppliers causes problems in achieving the required surface quality after turning. Therefore, the manufacturer needs to estimate the resulting quality before starting production, especially if it is an expensive, small-batch production from stainless [...] Read more.
The variability of the material properties of steel from different suppliers causes problems in achieving the required surface quality after turning. Therefore, the manufacturer needs to estimate the resulting quality before starting production, especially if it is an expensive, small-batch production from stainless steel. Predictive models will make it possible to estimate the surface roughness from the mechanical properties of steel and thus support decision making about supplier selection or acceptance of a material supply. This research presents a step-by-step decision-making procedure, which enables the trained staff to make quick decisions based on commonly available information in the Mill Test Certificate (MTC). A new multivariate second-order polynomial model and feedforward backpropagation artificial neural network (ANN) models have been developed using input variables from the MTC: Tensile Strength, Yield Strength, Elongation, and Hardness. Models were used to enhance the methodological robustness in formulating the decision if the predicted surface roughness is outside the required range, even before accepting the delivery. Both models can accurately predict surface roughness, while the ANN model is more accurate than the polynomial model; however, the predictive model is sensitive to the accuracy of the input data, and the model’s prediction is valid only under precisely defined conditions. Full article
(This article belongs to the Special Issue The Impact of Quality, Safety and Sustainability 4.0 on Manufacturing Processes)
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16 pages, 16901 KiB  
Article
Design and Implementation of Defect Detection System Based on YOLOv5-CBAM for Lead Tabs in Secondary Battery Manufacturing
by Jisang Mun, Jinyoub Kim, Yeji Do, Hayul Kim, Chegyu Lee and Jongpil Jeong
Processes 2023, 11(9), 2751; https://doi.org/10.3390/pr11092751 - 14 Sep 2023
Cited by 2 | Viewed by 953
Abstract
According to QYResearch, a global market research firm, the global market size of secondary batteries is growing at an average annual rate of 8.1%, but fires and casualties continue to occur due to the lack of quality and reliability of secondary batteries. Therefore, [...] Read more.
According to QYResearch, a global market research firm, the global market size of secondary batteries is growing at an average annual rate of 8.1%, but fires and casualties continue to occur due to the lack of quality and reliability of secondary batteries. Therefore, improving the quality of secondary batteries is a major factor in determining a company’s competitive advantage. In particular, lead taps, which electrically connect the negative and positive electrodes of secondary batteries, are a key factor in determining the stability of the battery. Currently, the quality inspection of secondary battery lead tab manufacturers mostly consists of visual inspection after vision inspection with a rule-based algorithm, which has limitations on the types of defects that can be detected, and the inspection time is increasing due to overlapping inspections, which is directly related to productivity. Therefore, this study aims to automate the quality inspection of lead tabs of secondary batteries by applying deep-learning-based algorithms to improve inspection accuracy, improve reliability, and improve productivity. We selected the YOLOv5 model, which, among deep-learning algorithms, has a benefit for object detection, and used the YOLOv5_CBAM model, which replaces the bottleneck part in the C3 layer of YOLOv5 with the Convolutional Block Attention Module (CBAM) based on the attention mechanism, to improve the accuracy and speed of the model. As a result of applying the YOLOv5_CBAM model, we found that the parameter was reduced by more than 50% and the performance was improved by 2%. In addition, image processing was applied to help segment the defective area to apply the SPEC value for each defective object after detection. Full article
(This article belongs to the Special Issue The Impact of Quality, Safety and Sustainability 4.0 on Manufacturing Processes)
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16 pages, 17762 KiB  
Article
Graphical Tools for Increasing the Effectiveness of Gage Repeatability and Reproducibility Analysis
by Jiří Plura, David Vykydal, Filip Tošenovský and Pavel Klaput
Processes 2023, 11(1), 1; https://doi.org/10.3390/pr11010001 - 20 Dec 2022
Cited by 1 | Viewed by 2257
Abstract
The article deals with the quality of measured data, which is necessary for effective quality management and successful implementation of the concept Industry 4.0 and the related concept Quality 4.0. The quality of the measured data is determined by the properties of the [...] Read more.
The article deals with the quality of measured data, which is necessary for effective quality management and successful implementation of the concept Industry 4.0 and the related concept Quality 4.0. The quality of the measured data is determined by the properties of the used measurement system, which are evaluated by measurement system analysis (MSA). Attention is paid to increasing the effectiveness of the repeatability and reproducibility analysis often used in practice. The importance of graphical tools of analysis, which are often neglected in practice, is emphasized in this regard, and new or modified graphical tools are proposed. The proposed graphical tools allow more detailed analysis of the data collected for the study and reveal the causes of the measurement system variability. The information obtained by applying these graphical tools is a valuable basis for proposals of appropriate actions to improve the measurement system. The use of the proposed graphical tools is presented in a real study of the repeatability and reproducibility of the measurement system. Full article
(This article belongs to the Special Issue The Impact of Quality, Safety and Sustainability 4.0 on Manufacturing Processes)
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